Phenotypic variability, a fundamental property of isogenic cell populations across all biological systems, creates challenges for medicine because it diversifies individual cell responses to treatments by introducing outliers that survive and resume the progression of the disease. So far, the mechanisms of phenotypic variability have been primarily studied in the context of developmental or regulatory pathways that produce discrete outcomes. The long-term goal of this project is to understand how complex dynamical functions can be tuned over a continuum by taking advantage of fluctuations in protein abundance. This project will build a quantitative understanding of the mechanisms and functional role of phenotypic variability for cellular dispersion and navigation using the well-characterized chemotaxis systems of E. coli and Salmonella as model systems. The central hypothesis is that cell-to-cell variability can resolve performance trade-offs of a single signaling pathway by creating individual cells with different capabilities, ensuring that subpopulations of cells will perform optimally in various environments and tasks. The experimental plan builds on a theoretical and computational framework established in previous works. An experimental platform recently developed in our lab allows for measurement of the trajectories of swimming cells and the abundance of fluorescently labeled proteins in the same live individual as they navigate controlled environments. Preliminary results indicate that quantitative relationships between protein quantity, behaviors, and chemotactic performance can be established at the single-cell level throughout the population. Iterative predictive modeling and experiments will extend current quantitative models to capture the cause and consequences of cell-to-cell variability on chemotactic behavior and population structure. Aim 1 will map distributions of chemotaxis protein levels onto distributions of individul diffusive behaviors and individual performance in exploratory or invasive tasks. Aim 2 will map chemotaxis protein abundance to chemotactic performance in static and time-varying chemical gradients to reveal the consequences of cell-to-cell variability for tracking various gradients. Ai 3 will characterize the trade-offs faced by individual cells in performing chemotaxis and examine whether cell-to-cell variability can alleviate these trade-offs at the population level. The experimental and theoretical framework developed for this project will have a broad impact on a fundamental challenge: to go beyond the characterization of average signaling network performance and to predict the consequences of fluctuations in molecular parameters on single-cell dynamical behaviors.